Automatic Segmentation of Polycystic Liver (ASEPOL)

Civil Hospices of Lyon

Status: Unknown

Conditions: Liver Injury; Polycystic Liver Disease; Polycystic Hepatorenal Disease

Treatments: Other: Training (2); Other: Validation (2); Other: Validation (1); Other: Anonymized CT examinations; Other: Training (1)

Study type: Observational

Funder types: Other

Identifiers

NCT03960710

ASEPOL

Details and patient eligibility

About

Assessing the volume of the liver before surgery, predicting the volume of liver remaining after surgery, detecting primary or secondary lesions in the liver parenchyma are common applications that require optimal detection of liver contours, and therefore liver segmentation.

Several manual and laborious, semi-automatic and even automatic techniques exist.

However, severe pathology deforming the contours of the liver (multi-metastatic livers...), the hepatic environment of similar density to the liver or lesions, the CT examination technique are all variables that make it difficult to detect the contours. Current techniques, even automatic ones, are limited in this type of case (not rare) and most often require readjustments that make automatisation lose its value.

All these criteria of segmentation difficulties are gathered in the livers of hepatorenal polycystosis, which therefore constitute an adapted study model for the development of an automatic segmentation tool.

To obtain an automatic segmentation of any lesional liver, by exceeding the criteria of difficulty considered, investigators have developed a convolutional neural network (artificial intelligence - deep learning) useful for clinical practice.

Enrollment: 120 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Patients ≥ 18 years old
• Patients with hepato-renal polycystosis, with or without surgery
• Patients with at least one abdominal-pelvic CT scan without injection or with injection between January 1, 2016 and August 2018
• Patients with good quality and available images

Exclusion criteria

• Patients with no CT scan images available
• Patients with bad quality of CT scan images

Trial design

120 participants in 2 patient groups

Neuronal network Training group

Description:

The following radiological variables, related to each CT examinations, will be collected for each patient: * Injection modalities (without injection, injected) * Major hepatectomy surgery * Importance of hepatic dysmorphia * Presence of intraperitoneal fluid effusion * Presence of renal polycystosis (especially on the right side).

Treatment:

Other: Training (2)

Other: Anonymized CT examinations

Other: Training (1)

Neuronal network Validation group

Description:

The following radiological variables, related to each CT examinations, will be collected for each patient: * Injection modalities (without injection, injected) * Major hepatectomy surgery * Importance of hepatic dysmorphia * Presence of intraperitoneal fluid effusion * Presence of renal polycystosis (especially on the right side).

Treatment:

Other: Anonymized CT examinations

Other: Validation (1)

Other: Validation (2)